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Energy and Buildings 76 (2014) 278–283

Contents lists available at ScienceDirect

Energy and Buildings

j ourna l ho me pa g e: www.elsev ier .com/ locate /enbui ld

ethod for the determination of optimal work environment in officeuildings considering energy consumption and human performance

hangzhi Dai, Li Lan, Zhiwei Lian ∗

tate Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

r t i c l e i n f o

rticle history:eceived 29 October 2013eceived in revised form 22 February 2014ccepted 26 February 2014

eywords:

a b s t r a c t

How to balance the contradiction between energy saving and improvement of indoor environmentalquality which consequently affects human performance has always been a problem. We put forward theeconomically optimum condition as a concept that maximized economic benefit in terms of regulationof office environment parameters. The calculation method was provided by which energy consump-tion could be reduced without compromise of human performance. A regression model predicting the

uilt environment designuman performancenergy consumptionptimal environment

energy consumption of a typical office building was illustrated as a function of two indoor environmentparameters, i.e., indoor air temperature and air ventilation rate. Practical factors including salary andelectric price, which have impact on the condition determination, were discussed. As a prototype, anoffice building in Shanghai achieved its economically optimal conditions at an air temperature of 25.1 ◦Cand an outdoor ventilation rate of 17.9 L/s-person in summer.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

The global energy use has rapidly grown in the past few decades,iving rise to more and more concerns over energy security, utiliza-ion efficiency and environmental impacts. It is also believed thathe energy situation would become more and more serious in theuture, especially in emerging economies [1,2]. Currently, buildingsccount for approximately 40% of the total energy consumptionlobally [1]. Forecast made by the EIA [3] suggests that energy usen the building environment will grow by 34% in the next 20 years.nergy consumption in HVAC systems, comprising heating, out-oor ventilation and air conditioning, has proved to be the largestnergy end-use both in residential and non-residential sectors. Inhina, for example, the HVAC systems are responsible for about 65%f the energy use in building sector [4]. As there are strong rela-ionships between HVAC energy consumption and indoor climateet point [5], reasonable indoor climate parameters are essentialor energy saving today. However, the relationship between HVACnergy consumption and indoor environment quality (IEQ) factorss difficult to be theoretically deduced because of its variance with

uilding envelope, HVAC performance, etc. Therefore, regressionnalysis and simulation methods have been widely used in thearametric studies of building energy consumption [6,7].

∗ Corresponding author. Tel.: +86 21 34204263.E-mail address: zwlian@sjtu.edu.cn (Z. Lian).

ttp://dx.doi.org/10.1016/j.enbuild.2014.02.077378-7788/© 2014 Elsevier B.V. All rights reserved.

In office buildings, however, energy consumption is not theonly priority for indoor climate design. Attentions are increasinglydrawn to the human–work environment interaction [8]. A healthyand effective built environment was proposed in the domain ofgreen ergonomics [9]. As the salary of office workers is an order ofmagnitude higher than the cost of maintaining and operating thebuilding [10], even small improvements in productivity can resultin a substantial economic benefit. Fisk and Rosenfeld [11] estimatedthat improved indoor environment can bring a direct increase inproductivity, ranging between 0.5% and 5%. Proper thermal con-dition [12–14] and indoor air quality [15–18] have proved to beof great help for better performance. However, little is known onthe combined effects of these factors. Qualitative studies have beenconducted by Witterseh et al. [19] on the combined effects of tem-perature and recorded noise. Hygge and Knez [20] investigated howoutdoor ventilation noise, air temperature, and illuminance com-bine or interact in their effects on cognitive performance. Clausenand Wyon [21] carried out an experiment with subjects exposedto different combinations of traffic noise, lighting, access to day-light, open-plan office noise, air temperature and air quality. Dueto the lacking of quantitative studies, the combined effects ofair temperature and ventilation rate on work performance wereroughly estimated in this paper, while attention should be paid on

the proposed methodology for determining economically optimumconditions.

Building services engineers gradually realize that not onlyenergy consumption but also human productivity should be

C. Dai et al. / Energy and Buildings 76 (2014) 278–283 279

iilosmhTot

2

pTibcor

m

wttmc

lwtdbi

fdrc

2

l

Fig. 1. A framework illustrating the core idea of the model.

ncorporated into the economic calculations pertaining to build-ng design and operation. However, there are few methods shedight on the global economic effects caused by energy consumptionf HVAC systems and human productivity in the building. In thistudy we proposed a method to achieve such economically opti-al indoor environment by balancing the contradiction between

uman performance and energy consumption of HVAC systems.wo important IEQ factors including indoor air temperature andutdoor ventilation rate were set as the example to illustrate howo achieve such economically optimal set points.

. Methods

Improved indoor environment generally brings forth higherroductivity while may cause extra investment in operating cost.o obtain economically optimum conditions, both the benefit frommproved performance and the corresponding energy cost shoulde quantitatively analyzed. A subtraction model instead of theost–benefit ratio was selected in Eq. (1) since energy charge wasnly a small part in total inputs and much less than the economiceturns.

ax G(T, Q, E. . .) − C(T, Q, E. . .) (1)

here T, Q, E. . . were the IEQ factors, T was indoor air tempera-ure, Q was outdoor ventilation rate, E was illuminance level, G washe economic returns decided by the employee working perfor-

ance and influenced by the IEQ factors, C was the HVAC energyonsumption which also depended on the IEQ factors.

The core idea of the model was illustrated in Fig. 1. It workedike an open-loop control strategy. Two functions representing the

ork output and energy cost of the certain office were processedo build the optimization model as shown in Eq. (1). And the inputata of the whole model decided what the two functions woulde. After using the optimization method and theory, the optimal

ndoor environment conditions could be achieved.Due to the lack of quantitative relationship between human per-

ormance and other IEQ factors, two important parameters wereiscussed in this study: air temperature T and outdoor ventilationate Q. Nuisance factors affecting either economic returns or energyonsumption were kept constant here.

.1. The relationship between office work output and IEQ factors

Taking air temperature as its horizontal axis and outdoor venti-ation rate as the vertical axis, a Cartesian plane could be drawn in

Fig. 2. Method to obtain the relative performance in different conditions.

which each point had its corresponding value of work performance,as shown in Fig. 2. The origin point (T0, Q0) was the reference pointwhere the relative performance was set to be 1. Both T0 and Q0 wereset at the values when human performance was poorest. Assumingthe magnitude of the combined effects was the sum of indepen-dent parameters, RPH represented the relative performance, butif the combined effects were replaced by the greater of the sin-gle parameters, the relative performance was then expressed asRPL. At present there was a study reporting the combined effectof outdoor ventilation and temperature on human performance.Wargocki and Seppänen [22] suggested that the magnitude of thecombined effect was at least the effect of the greater of the singleparameters, and not more than the sum of the independent param-eters. So we proposed the mean value of RPH and RPL as the finalrelative performance (Eq. (2)). ˛1 and ˛2 represented the changein relative performance when the indoor environment improvedalong with the coordinate axes.

RPx = 12 × (RPH + RPL) = 1 + 1

2 × [˛1 + ˛2 + max(˛1, ˛2)]

= 12 × [RPt + RPq + max(RPt, RPq) − 1] (2)

Given the varying trends of the relative performance along coor-dinate axes, the relative performance interval from RPL to RPH couldbe figured out for the office environment conditions (Tx, Qx).

As shown in Fig. 2, when the air temperature was kept to beconstant, the relative performance would be RPq. if the outdoor ven-tilation rate increased from Q0 to Qx L/s-person. Similarly, when theoutdoor ventilation rate was kept to be Qx L/s-person, the relativeperformance would be RPt by reducing the air temperature from T0to Tx

◦C.The effects of air temperature or outdoor ventilation rate on

human performance have been estimated in existing studies. Inthis paper, the quantitative relationship between productivity andthermal sensation vote (Eq. (3)) developed by Lan et al. [23] wasused. Based on this relationship, the economic optimization modelcould illustrate the optimal conditions for different seasons andcould allow for the changes of factors including clothing thermalresistance, relative humidity and so on [24].

RP = −0.0351 · tsv3 − 0.5294 · tsv2 − 0.215 · tsv + 99.865 (3)

where RP was the relative performance when compared to the max-imum performance and tsv was the thermal sensation vote (−3 to+3 on the ASHRAE seven-point thermal sensation scale) [23].

Seppänen et al. [15] established a quantitative relationship(Fig. 3) between outdoor ventilation rate and productivity. The

following equation (Eq. (4)) could be obtained from Fig. 3.

RPq = 0.021 · ln(Q ) + 0.960 (6.5 L/s-person ≤ Q ≤ 30 L/s-person)

(4)

280 C. Dai et al. / Energy and Buildings 76 (2014) 278–283

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which is an integrated building simulation tool using base tempera-tures to couple buildings and systems, and carrying out “simulationby stage” for building environment and its control system [26].

Table 1Main input data and output results of the model in case study.

Input:Weather data:Reference year weather data of Shanghai (in practical use, real weather

conditions are better)

Building enclosure parameters:Open plan offices area, 454 m2; private offices areas, 21 m × 6 m; floor

size, 54 m × 16.8 m × 3.3 m; outer wall material, porous concrete;inner wall material, aerated concrete slab; windows, 6 mm singleglass, 145.2 m2 in area; rooms air leakage, 0.4 h−1, etc.

Heat sources:68 persons in open plan offices, 6 persons in private offices, 70 W for

each person, 22 W/m2 for the light and facility in open plan officesand 30 W/m2 in private offices

Staff/facility schedules:5 days a week and 8 h a day.On weekdays, 50% of the staff stayed in the office from 12:00 pm to

1:00 pm and 5:00 pm to 6:00 pm; 10% of the staff worked overtimefrom 6:00 pm to 9:00 pm

HVAC system parameters:All air VAV system and screw refrigerator

Thermal sensation factors:Air temperature, T; mean radiant temperature, T; relative humidity,

40%; air velocity, 0.15 m/s; clo value, 0.6 clo; activity level, 1.2 m

Electric price:1.2 RMB/KWh

Pay levels:8000 RMB/month-person in the open plan offices and

15,000 RMB/month-person in the private offices

Date:

Fig. 3. Relative performance in relation to outdoor ventilation rate [15].

n which RPq was the relative performance comparing to theeference outdoor ventilation rate of 6.5 L/s-person. The outdoorentilation rate was controlled below 30 L/s-person considering theestrictions of the HVAC design and installation.

Assuming that the staff cost was taken as surrogate of the worketurns at the referenced temperature and outdoor ventilation rate,he economic output of employee’s working performance could bealculated after the reference point (T0, Q0) was determined (Eq.5)):

(T, Q ) = s · RPx (5)

here s was staff cost when relative performance was equal to 1nd RPx was given by Eqs. (2)–(4).

.2. The relationship between HVAC energy consumption and IEQactors

Energy consumption of the building was calculated with simu-ation tool since it could vary with different building enclosures andVAC systems, especially considering the interaction of tempera-

ure and outdoor ventilation. A typical office building in Shanghaias taken as an example in this case study. Based on the calcu-

ated energy consumption for different temperatures and outdoorentilation rates, a relationship between energy consumption andndoor parameters (Eq. (6)) was developed by binary polynomialegression, since multiple polynomial regression models for energyse in air-conditioned office buildings have been proved valid [25].

(T, Q ) = e · Pn(T, Q ) (6)

here e was the local electricity price which reflected the energyemand directly and Pn(T, Q) was the polynomial regression func-ion describing how energy consumption of HVAC system changedlong with temperature and outdoor ventilation.

.3. The optimization problem solver

In this case study the temperature was constrained between0 ◦C and 28 ◦C and the outdoor ventilation rate ranged from 6.5 L/s-erson to 30 L/s-person. Using the penalty function, the constrainedinary optimization problem as expressed in Eq. (1) was transferredo an unconstrained problem (Eq. (7)):

in �(T, Q, r) = C(T, Q ) − G(T, Q ) + r

·[

1T − 20

+ 128 − T

+ 1Q − 6.5

+ 130 − Q

](7)

here r was the penalty factor.

Fig. 4. Floor plan of the building.

Then the steepest descent method was used to determine thedirection of search in Eq. (8); the step size was calculated withArmijo method. The iteration process was finished on Matlab toachieve the points of convergence, which represented the optimumwork conditions for temperature and outdoor ventilation rate.

d = −(

∂�

∂T,

∂�

∂Q

)(8)

3. Case study

A prototypical office building in Shanghai was used in this casestudy. One floor of the typical office was selected for the detailedanalysis. The floor plan was shown in Fig. 4. Hourly simulation wasperformed for the summertime energy consumption with DEST-C,

May 22–September 27

Output:Optimal air temperature T = 25.1 ◦COptimal outdoor ventilation rate Q = 17.9 L/s-person

C. Dai et al. / Energy and Buildings 76 (2014) 278–283 281

Table 2the simulated energy consumption of the HVAC system with five temperature levels: 20, 22, 24, 26, 28 ◦C and five ventilation rates: 6.5, 8, 10, 15, 20 L/s-person (KWh).

Ventilation rate (L/s-person) Temperature (◦C) 20 22 24 26 28

6.5 Fan in VAV system 9657.12 9440.97 8483.84 7164.21 6218.61Chiller 15085.25 14710.11 14210.76 13561.12 13046.34Chilled water pump 4131.58 4120.09 4020.44 3915.04 3780.90Cooling water pump 12218.65 12160.25 11844.76 11509.25 11105.82Cooling tower 3167.25 3158.43 3082.04 3001.24 2898.41Total 44259.85 43589.86 41641.85 39150.87 37050.09

8 Fan in VAV system 10897.03 10496.72 9608.07 8135.55 7296.28Chiller 15236.39 14749.25 14250.85 13747.87 13398.00Chilled water pump 4131.58 4118.17 4039.60 3955.28 3849.89Cooling water pump 12279.05 12155.93 11900.06 11635.23 11318.87Cooling tower 3167.25 3156.96 3096.73 3032.09 2951.30Total 45711.30 44677.03 42895.31 40506.03 38814.34

10 Fan in VAV system 12692.59 12639.57 11527.50 10011.45 8949.81Chiller 15427.80 14914.02 14417.71 14024.65 13772.86Chilled water pump 4129.67 4120.09 4066.43 3982.11 3915.04Cooling water pump 12294.56 12170.75 11985.57 11728.28 11523.66Cooling tower 3165.78 3158.43 3117.30 3052.66 3001.24Total 47710.40 47002.86 45114.52 42799.15 41162.62

15 Fan in VAV system 18948.10 18594.56 17827.04 16249.37 15055.70Chiller 15743.32 15336.51 14955.23 14731.30 14405.53Chilled water pump 4129.67 4120.09 4093.26 4035.77 3953.37Cooling water pump 12247.64 12196.00 12095.55 11920.06 11669.57Cooling tower 3165.78 3158.43 3137.86 3093.79 3030.63Total 54234.51 53405.59 52108.93 50030.29 48114.79

20 Fan in VAV system 30383.40 30051.77 29608.35 27998.43 26582.50Chiller 16504.80 16057.45 15627.05 15500.18 15175.66Chilled water pump 4127.75 4125.84 4091.34 4049.18 3982.11

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performance model. For the reason that this paper concentrates onintroducing a new methodology instead of exactly what the optimalconditions should be, the work performance model is simplifiedwith an assumption that the combined effects of temperature and

Cooling water pump 12276.87Cooling tower 3164.31Total 66457.13

Prime input data were listed in Table 1. The HVAC energy con-umptions were calculated when the design temperatures were 20,2, 24, 26 and 28 ◦C and the design outdoor ventilation rates were.5, 8, 10, 15 and 20 L/s-person. The total 25 different conditionsnd their simulation results were listed in Table 2.

The regressed HVAC energy cost function in Fig. 1 during theummer time could be expressed by Eq. (9).

= 14.304 · T3 + 6.025 · Q 3 − 1.1124 · T2 · Q − 0.886 · T · Q 2

− 1076.480 · T2 − 118.343 · Q 2 + 94.912 · T · Q + 25398.300

·T + 657.731 · Q − 148174.000 (9)

2 equaled to 1.000, which meant three order polynomial regres-ion model had a preferable effect here.

By substituting the thermal sensation factors given in Table 1nto Eq. (3), the relative performance could then be expressed as aunction of air temperature T (Eq. (10)).

Pt = −1.151 × 10−5 · T3 + 4.045 × 10−4 · T2 + 5.053 × 10−4

·T + 0.921 (10)

here RPt was the performance relative to temperature 28 ◦C athich staff performance was set to be 100%. The maximum perfor-ance happened when air temperature was 24 ◦C.Thus, the work output shown in Fig. 1 could be evaluated by

taff cost in Eq. (11):

= 256

· (8000 × 68 + 15000 × 6) · RPx (11)

here 25/6 represented 4 months and 5 days; 68 was the staffumber in open plan offices; 6 was the staff number in private

ffices; RPx was delivered in Eq. (2).

Then substituting C(T, Q) and G(T, Q) in Eq. (7) with Eqs. (9)nd (11), the economic optimum conditions could be obtained bysing the method described in Section 2.3. The results were given in

12247.41 12124.91 11997.53 11790.293162.84 3136.40 3104.08 3052.66

65645.31 64588.05 62649.41 60583.22

Table 1. As shown in Fig. 5, the order of magnitude of the improve-ment in economic returns in studied domain is around ten thousandRMB for the single office floor in summer time, which has demon-strated the great social benefit from the method.

4. Discussion

For quite some time, there has always been an argument aroundthe standard of energy conservation for public buildings. The eco-nomic optimum conditions put forward in this paper can be a con-vincible method for policy-making as both energy saving and workefficiency are taken into consideration. The method consists ofthree parts: the energy consumption model of the HVAC system, thework performance model and the economic optimization model.The accuracy of this whole method mainly depends on the work

Fig. 5. The relative value of economic returns depending on staff working minusenergy consumption cost in different office conditions.

282 C. Dai et al. / Energy and Buildings 76 (2014) 278–283

Fig. 6. The optimal office conditions with different staff costs (s) and electric price (e) (the value 0 means the ratio s/e is the same with that in the case study, while 50%means the s/e ratio grows by 50% of the value in case study).

Table 3Comparisons between the final results when different factors are considered.

Factors in consideration Performance and energy Performance Energy Comfort

ger, th

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Air temperature (◦C) 25.1 24

Outdoor ventilation rate (L/s-person) 17.9 The lar

utdoor ventilation rate on relative performance were the middlealue of the interval. Consequently, great effort have to be made inuture studies to investigate how multiple indoor parameters influ-nce work performance and how to evaluate the effects of IEQ onork performance in real office. Only the effects of air tempera-

ure and outdoor ventilation rate were investigated in this study,ue to the lack of quantitative relationship between human per-ormance and other indoor climate parameters. Studies to quantifyhe effects of other parameters including noise, illumination, etc. onuman performance should be intensified, as Omer [1] claimed thathe provision of good indoor environmental quality while achievingnergy and cost-efficient operation in buildings represents a multi-ariant problem. To make the optimum model more widely used,elated information of building enclosure and HVAC system will bedded up to the variable parameters with annual construction costnd maintenance cost taken into consideration.

It can be deduced from the economic optimization modelingEq. (1)) that either the change of electric price (e), which repre-ents the energy cost regulated by the government, or the changef staff costs (s), which represents the created economic value, willead to different optimal office conditions. However, when the ratiof staff costs s to electric price e remains unchanged, the optimalonditions stay the same. The influence of staff costs (s) and electricrice (e) on optimal indoor conditions is shown in Fig. 6. When the/e ratio increases, optimal air temperature will converge towardhe maximal productivity temperature at 24 ◦C and the optimal out-oor ventilation rate will rise with a diminishing speed. If the s/eatio in an office increases by 200%, the optimal outdoor ventilationate will increase by around 7 L/s-person. Actually, the economicptimum conditions are not stable and influenced by several fac-ors such as the nature of the company, the building constructiononditions, the energy price, etc. Consequently, relevant standardhould not confine the indoor conditions to be some certain val-es. For instance, in summer the indoor temperature should beet above 26 ◦C for commercial air-conditioned buildings in China,hich in fact brings enormous economic losses due to the decrease

f work performance.It might provide a better view if a comparison was made

etween the economically optimum conditions and the resultshen a single factor is considered. Taking the case study as

No air-conditioning 23–26.5 (PMV is between −0.5 and 0.5)e better 0 The larger, the better

example, Table 3 lists the prime values of air temperature andoutdoor ventilation rate when contemplating different factors.Results demonstrate the inappropriateness when only perfor-mance, energy or comfort is taken into account.

Better indoor environment not only improves work perfor-mance but also reduces sick leaves. However, the number ofrelevant quantitative studies is limited, which has restricted theaccuracy of the model in this paper. Still, some preliminary con-clusions could be drawn from previous studies that increasedtemperatures and reduced outdoor ventilation are linked to theprevalence of building-related SBS-symptoms [27–30]. With dou-bling the outdoor air supply, a 10% reduction in illness and 1.5%improvement on average for the work performance are suggested[22]. Considering the fact that the relative risk for short-term sickleave was 1.53 among office workers with lower outdoor ventila-tion [29], the productivity will increased by 1.66% instead of 1.5%for doubling of outdoor air supply, which indicates that the effectweight of human productivity in the optimum model was slightlyunderestimated and the optimal outdoor ventilation rate couldbe larger than what the method suggests. However, whether theoptimal temperature should be slightly lower than the value deter-mined by our method remains uncertain because it is not clear yetwhat the effects of lower temperature on sick leave should be.

5. Conclusions

(1) The economical optimum indoor conditions were put forwardas a way to ascertain the best set points for multiple IEQ factors.The methodology on how to obtain the optimum parametersfor temperature and outdoor ventilation rate in air-conditionedoffice buildings was introduced.

(2) The economic optimal conditions change with several factorssuch as the nature of the company, the building constructionconditions, the energy price, etc. So the prescribed parametersfor the regulation of air conditioning systems in public placesshould be set within different ranges classified for different

nature of business, energy saving levels of buildings and HVACsystems.

(3) The economical optimum indoor conditions depend largely onthe ratio of staff costs to electric price. When the ratio increases,

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C. Dai et al. / Energy and

optimal air temperature will converge toward the maximal pro-ductivity temperature and the optimal outdoor ventilation ratewill rise with a diminishing speed. Thus this ratio may also betaken as an indicator in specifying parameters for the regulationof air conditioning systems in public places.

cknowledgement

This work is financially supported by the key project of Nationalatural Science Foundation of China (No. 51238005).

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